AUTOMATED LITERATURE META ANALYSIS USING HYPOTHESIS GENERATORS AND AUTOMATED SEARCH

20220319656

17/633701

August 16, 2020

Provided herein are methods and systems for automated generation of hypothesis based on sets of search terms, and scoring of said automatically generated hypothesis to determine novelty, reasonability and/or feasibility thereof. Further provided are methods of utilizing said generated hypothesis for determination of personalized treatment regime of various health conditions.

TECHNION RESEARCH & DEVELOPMENT FOUNDATION (Haifa, IL)

1. A computer implemented method for generating and ranking of hypotheses, based on a set of search terms, the method comprising: obtaining two or more sets of search terms; generating a plurality of combinations of search terms from the sets, each combination corresponding to a hypothesis; for each of the plurality of combinations of search terms, searching on one or more electronic databases for the combination, thereby obtaining a number of publications (NOP) corresponding to the respective hypothesis; generating a matrix with components indexed according to the hypotheses, each component assigned a value equal to the NOP of the combination of search terms corresponding to the respective hypothesis; sorting the matrix according to one or more sorting criteria; and ranking at least some of the hypotheses based on the sorted matrix, wherein the ranking is indicative of at least one of a degree of novelty, a degree of feasibility, and a degree of reasonability of the hypotheses.

2. The method of claim 1, further comprising a step of performing an additional search using a second set of search terms or search variables on the sorted NOP matrix of the one or more selected generated hypotheses, to thereby generate a comparison matrix between the sorted NOP matrix and the results of the additional search.

3. The method of claim 1, further comprising presenting one or more of the matrix of the NOP, the sorted matrix of the NOP, and the ranking of the selected generated hypotheses.

4. The method of claim 1, wherein the hypothesis is a scientific hypothesis.

5. The method of claim 1, wherein each search term is at least one of a word, list of words, a sentence, a generic term, and a question.

6. The method of claim 1, wherein the selected combination of the search is structured as at least one of “one vs. many” and “many vs. many.”

7. The method of claim 1, wherein the search is performed using a web crawler, a web scraper, or an automated search tool.

8. The method of claim 1, wherein the electronic database is one of PubMed, Google Scholar, clinicaltrials.gov, Embase, and Semantic Scholars.

9. The method of claim 1, wherein the NOP matrix is visualized using a visual coding having adjustable threshold, based on the visualization parameters.

10. The method of claim 1, wherein the degree of reasonability comprises at least one of local reasonability (LR), horizontal reasonability (HR), and vertical reasonability (VR).

11. The method of claim 10, wherein the degree of reasonability further comprises at least one of extended horizontal reasonability (THR) and extended vertical reasonability (TVR).

12. The method of claim 10, wherein at least one of the degree of feasibility and the degree of reasonability are determined based on an adjustable threshold of number of publications.

13. The method of claim 12, wherein the adjustable threshold is user defined.

14. The method of claim 1, further comprising providing a numerical score based on the ranking of the hypothesis.

15. The method of claim 1, for identifying the temporal occurrence of hypotheses.

16. The method of claim 1, further comprising identifying the geographical distribution of hypotheses.

17. A computer implemented method for generation and ranking of hypotheses, based on a set of search terms, the method comprising: obtaining a set of two or more search terms; generating multiple hypotheses, based on a selected combination of the search terms; performing a search for the generated hypotheses on one or more databases stored on a server, to determine the number of publications (NOP) for each generated hypothesis; generating a matrix of the NOP of one or more selected generated hypotheses; sorting the NOP matrix of the one or more selected generated hypotheses, based on one or more sorting parameters; and ranking the selected generated hypotheses based on the NOP matrix, wherein the ranking is indicative of at least one of the degree of novelty, a degree of feasibility, and a degree of reasonability of the selected generated hypothesis.

18. (canceled)

19. The method of claim 17 further comprising a user interface unit, a display unit and a communication unit.

20. (canceled)

21. A computer implemented method for determining a personalized high resolution treatment regime of a patient afflicted with a disease, the method comprising: obtaining a set of two or more search terms related to the disease of the patient; generating multiple hypotheses related to treatment of the disease, based on a selected combination of the search terms; performing a search for the generated hypotheses on one or more suitable databases stored on a server, to determine the number of publications (NOP) for each generated hypothesis; generating a matrix of the NOP of one or more selected generated hypotheses; sorting the NOP matrix of the one or more selected generated hypotheses, based on one or more sorting parameters; ranking the selected generated hypotheses based on the NOP matrix, wherein the ranking is indicative of at least one of a degree of novelty, a degree of feasibility, and a degree of reasonability of the selected generated hypothesis, to determine a first treatment; repeating the search for one or more times with search terms related to at least one of the disease and the first treatment, to determine an additional one or more treatments; and determining, based on the identified treatments, a personalized treatment regime for said patient.

22. The method according to claim 19, wherein the treatment is a combination therapy.

23. The method according to claim 19, wherein the patient is a cancer patient.

24. The method according to claim 21, wherein at least one of the first treatment and the one or more additional treatments are selected from at least one of a drug, an immunotherapy, a surgical procedure, radiotherapy, chemotherapy, psychotherapy, and lifestyle therapy.

25. The method according to claim 22, wherein the immunotherapy is one of antibodies based therapy and engineered T-cells.

26. The method according to claim 19, wherein the treatment regime further includes a spatial distribution sequence of at least one of the first and additional treatment.

27. The method according to claim 19, wherein the treatment regime further includes a nanoparticle formulation of at least one of the first and additional pharmacological treatment.

28. A computer implemented method for determining a personalized high resolution treatment regime of a patient afflicted with a disease, the method comprising: obtaining two or more sets of search terms; generating a plurality of combinations of search terms from the sets, each combination corresponding to a hypothesis related to treatment of the disease; for each combination of search terms, searching on one or more electronic databases for the combination, thereby obtaining a number of publications (NOP) corresponding to the respective hypothesis; generating a matrix with components indexed according to the hypotheses, each component assigned a value equal to the NOP of the combination of search terms corresponding to the respective hypothesis; sorting the matrix according to one or more sorting criteria; and ranking at least some of the hypotheses based on the sorted matrix, wherein the ranking is indicative of at least one of a degree of novelty, a degree of feasibility, and a degree of reasonability of the hypotheses, to determine a first treatment; repeating the search for one or more times with search terms related to at least one of the disease and the first treatment, to determine an additional one or more treatments; and determining, based on the identified treatments, a personalized treatment regime for said patient.

29. The method according to claim 26, wherein the treatment is a combination therapy.

30. The method according to claim 26, wherein the patient is a cancer patient.

31. The method according to claim 28, wherein at least one of the first treatment and the one or more additional treatments are selected from: a drug, an immunotherapy, a surgical procedure, radiotherapy, chemotherapy, psychotherapy, and lifestyle therapy.

32. The method according to claim 29, wherein the immunotherapy is one of antibodies based therapy and engineered T-cells.

33. The method according to claim 24, wherein the treatment regime further includes a spatial distribution sequence of at least one of the first and additional treatment.

34. The method according to claim 26, wherein the treatment regime further includes a nanoparticle formulation of at least one of the first and additional pharmacological treatment.

35. A system for automated generation of a hypothesis comprising a processor configured to: obtain two or more sets of search terms; generate a plurality of combinations of search terms from the sets, each combination corresponding to a hypothesis; for each of the plurality of combinations of search terms, search on one or more electronic databases for the combination, thereby obtaining a number of publications (NOP) corresponding to the respective hypothesis; generate a matrix with components indexed according to the hypotheses, each component assigned a value equal to the NOP of the combination of search terms corresponding to the respective hypothesis; sort the matrix according to one or more sorting criteria; and rank at least some of the hypotheses based on the sorted matrix, wherein the ranking is indicative of at least one of a degree of novelty, a degree of feasibility, and a degree of reasonability of the hypotheses.

36. The system of claim 33, wherein the processor is further configured to perform an additional search using a second set of search terms or search variables on the sorted NOP matrix of the one or more selected generated hypotheses, to thereby generate a comparison matrix between the sorted NOP matrix and the results of the additional search.

37. The system of claim 33, wherein the processor is further configured to present one or more of the matrix of the NOP, the sorted matrix of the NOP, and the ranking of the selected generated hypotheses.

38. The system of claim 33, wherein the hypothesis is a scientific hypothesis.

39. The system of claim 33, wherein each search term is at least one of a word, list of words, a sentence, a generic term, and a question.

40. The system of claim 33, wherein the selected combination of the search is structured as at least one of “one vs. many” and “many vs. many.”

41. The system of claim 33, wherein the search is performed using a web crawler, a web scraper, or an automated search tool.

42. The system of claim 33, wherein the electronic database is one of PubMed, Google Scholar, clinicaltrials.gov, Embase, and Semantic Scholars.

43. The system of claim 33, wherein the NOP matrix is visualized using a visual coding having adjustable threshold, based on the visualization parameters.

44. The system of claim 33, wherein the degree of reasonability comprises at least one of local reasonability (LR), horizontal reasonability (HR), and vertical reasonability (VR).

45. The method of claim 42, wherein the degree of reasonability further comprises at least one of extended horizontal reasonability (THR) and extended vertical reasonability (TVR).

46. The system of claim 42, wherein at least one of the degree of feasibility and the degree of reasonability are determined based on an adjustable threshold of number of publications.

47. The system of claim 44, wherein the adjustable threshold is user defined.

48. The system of claim 33, wherein the processor is further configured to provide a numerical score based on the ranking of the hypothesis.

49. The system of claim 33, wherein the processor is further configured to identify the temporal occurrence of hypotheses.

50. The system of claim 33, wherein the processor is further configured to identify the geographical distribution of hypotheses.

51. A non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to: obtain two or more sets of search terms; generate a plurality of combinations of search terms from the sets, each combination corresponding to a hypothesis; for each of the plurality of combinations of search terms, search on one or more electronic databases for the combination, thereby obtaining a number of publications (NOP) corresponding to the respective hypothesis; generate a matrix with components indexed according to the hypotheses, each component assigned a value equal to the NOP of the combination of search terms corresponding to the respective hypothesis; sort the matrix according to one or more sorting criteria; and rank at least some of the hypotheses based on the sorted matrix, wherein the ranking is indicative of at least one of a degree of novelty, a degree of feasibility, and a degree of reasonability of the hypotheses.

52. The non-transitory computer readable medium of claim 49, wherein the processor is further caused to perform an additional search using a second set of search terms or search variables on the sorted NOP matrix of the one or more selected generated hypotheses, to thereby generate a comparison matrix between the sorted NOP matrix and the results of the additional search.

53. The non-transitory computer readable medium of claim 49, wherein the processor is further caused to present one or more of the matrix of the NOP, the sorted matrix of the NOP, and the ranking of the selected generated hypotheses.

54. The non-transitory computer readable medium of claim 49, wherein the hypothesis is a scientific hypothesis.

55. The non-transitory computer readable medium of claim 49, wherein each search term is at least one of a word, list of words, a sentence, a generic term, and a question.

56. The non-transitory computer readable medium of claim 49, wherein the selected combination of the search is structured as at least one of “one vs. many” and “many vs. many.”

57. The non-transitory computer readable medium of claim 49, wherein the search is performed using a web crawler, a web scraper, or an automated search tool.

58. The non-transitory computer readable medium of claim 49, wherein the electronic database is one of PubMed, Google Scholar, clinicaltrials.gov, Embase, and Semantic Scholars.

59. The non-transitory computer readable medium of claim 49, wherein the NOP matrix is visualized using a visual coding having adjustable threshold, based on the visualization parameters.

60. The non-transitory computer readable medium of claim 49, wherein the degree of reasonability comprises at least one of local reasonability (LR), horizontal reasonability (HR), and vertical reasonability (VR).

61. The non-transitory computer readable medium of claim 58, wherein the degree of reasonability further comprises at least one of extended horizontal reasonability (THR) and extended vertical reasonability (TVR).

62. The non-transitory computer readable medium of claim 58, wherein at least one of the degree of feasibility and the degree of reasonability are determined based on an adjustable threshold of number of publications.

63. The non-transitory computer readable medium of claim 60, wherein the adjustable threshold is user defined.

64. The non-transitory computer readable medium of claim 49, wherein the processor is further caused to provide a numerical score based on the ranking of the hypothesis.

65. The non-transitory computer readable medium of claim 49, wherein the processor is further caused to identify the temporal occurrence of hypotheses.

66. The non-transitory computer readable medium of claim 49, wherein the processor is further caused to identify the geographical distribution of hypotheses.

16; 16; 06

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